A motor power distribution system automatic switching control method and system

Abstract

This invention discloses an automatic switching control method and system for a motor power distribution system. The normally closed auxiliary contacts of a vacuum circuit breaker are connected in series to a coupler. When the contact state changes, an insulation monitoring device is triggered to switch operating modes. The insulation monitoring device acquires the electric field intensity distribution and leakage current path of the motor's insulation medium, constructs an electric field-impedance coupling spectrum, and identifies areas of concentrated field strength. Cluster analysis is performed at the point of minimum field strength to form dominant nodes, candidate paths are established, and the path with the smallest impedance gradient is selected as the switching path. The arc characteristic parameters of the vacuum circuit breaker are monitored, and the arc energy density is extracted, converted into magnetic field energy storage, and released as a back electromotive force. The switching position is corrected by extracting the attenuation coefficient through a buffer voltage. An impedance matching window is generated, and a comprehensive switching control command is generated and executed by combining optimized switching time and energy recovery coefficient. This achieves intelligent control of the switching position and time, while simultaneously recovering arc energy, improving system safety and economy.

Description

Technical Field

[0001] This invention relates to the field of motor power distribution system control technology, and in particular to an automatic switching control method and system for motor power distribution systems. Background Technology

[0002] As a core power equipment in industrial production, the level of intelligence in the switching control of motor power distribution systems directly affects the safety and economy of system operation. Traditional power distribution switching methods mainly rely on manual judgment and preset program control, lacking a real-time monitoring mechanism based on the auxiliary contact status of vacuum circuit breakers. This makes it difficult to accurately sense changes in the motor's operating status and respond promptly, and also lacks in-depth analysis capabilities regarding the motor's insulation status and electric field distribution.

[0003] Existing technologies generally suffer from low energy utilization efficiency during switching processes. A large amount of energy generated by arc discharge is directly consumed without effective recovery and utilization. At the same time, the selection of switching time lacks scientific basis, often leading to problems such as excessive electrical shock and shortened equipment life. In addition, traditional methods cannot achieve dynamic optimization and adjustment of switching positions, and are insufficiently adaptable to load fluctuations and environmental changes, making it difficult to meet the urgent needs of modern industry for high reliability, high efficiency and intelligence in power distribution systems. Summary of the Invention

[0004] This invention discloses an automatic switching control method and system for a motor power distribution system. It establishes a monitoring mechanism through the auxiliary contacts of a vacuum circuit breaker, constructs an electric field-impedance coupling spectrum to identify areas of concentrated field strength, plans a switching path at the point of minimum field strength, monitors arc characteristic parameters to realize the conversion of magnetic field energy storage into reverse electromotive force, and combines the buffer voltage attenuation coefficient to correct the switching position and impedance matching window to determine the switching time. It generates a comprehensive control command that integrates spatial, temporal, and energy information to achieve intelligent automatic switching.

[0005] The first aspect of this invention provides an automatic switching control method for a motor power distribution system, comprising the following steps:

[0006] An insulation monitoring device is used to obtain the electric field intensity distribution and leakage current path of the motor insulation medium. The field intensity gradient is extracted from the electric field intensity distribution, and the impedance change trajectory is tracked along the leakage current path. The field intensity gradient and the impedance change trajectory are superimposed to form an electric field-impedance coupling spectrum.

[0007] The electric field-impedance coupling spectrum is used to identify the field strength concentration region, the minimum field strength point is searched in the field strength concentration region, the switching path is planned through the minimum field strength point, and the first switching position is determined according to the impedance distribution on the switching path.

[0008] The arc characteristic parameters of the normally closed auxiliary contact of the vacuum circuit breaker are monitored when it breaks. The arc energy density is separated from the arc characteristic parameters, the arc energy density is converted into magnetic field energy storage, and an energy recovery coefficient is generated based on the accumulation rate of the magnetic field energy storage.

[0009] Based on the impedance change trajectory, the real-time load impedance and power supply line impedance are extracted. The difference between the real-time load impedance and the power supply line impedance is analyzed to generate an impedance matching window. The energy recovery coefficient and the impedance matching window are combined to determine the optimal switching time.

[0010] The magnetic field stores energy and releases a back electromotive force at the optimized switching moment. The amplitude of the back electromotive force is modulated to generate a buffer voltage. An attenuation coefficient is extracted from the buffer voltage. The first switching position is corrected according to the attenuation coefficient to form a second switching position.

[0011] Based on the second switching position, the optimized switching time, and the energy recovery coefficient, a comprehensive switching control command is generated, and the comprehensive switching control command is executed to complete the automatic switching of the motor power distribution system.

[0012] A second aspect of the present invention provides an automatic switching control system for a motor power distribution system, comprising:

[0013] The electric field monitoring module is used to acquire the electric field intensity distribution and leakage current path of the motor insulation medium using an insulation monitoring device, extract the field intensity gradient from the electric field intensity distribution, track the impedance change trajectory along the leakage current path, and superimpose the field intensity gradient and the impedance change trajectory to form an electric field-impedance coupling spectrum.

[0014] The path planning module is used to identify the field strength concentration area through the electric field-impedance coupling spectrum, search for the minimum field strength point in the field strength concentration area, plan a switching path through the minimum field strength point, and determine the first switching position according to the impedance distribution on the switching path.

[0015] An energy recovery module is used to monitor the arc characteristic parameters when the normally closed auxiliary contact of a vacuum circuit breaker breaks, separate the arc energy density from the arc characteristic parameters, convert the arc energy density into magnetic field energy storage, and generate an energy recovery coefficient based on the accumulation rate of the magnetic field energy storage.

[0016] The timing determination module is used to extract the real-time load impedance and power supply line impedance based on the impedance change trajectory, perform difference analysis on the real-time load impedance and the power supply line impedance to generate an impedance matching window, and combine the energy recovery coefficient with the impedance matching window to determine the optimal switching time.

[0017] The position correction module is used to release a back electromotive force at the optimized switching time by using the energy stored in the magnetic field, modulate the amplitude of the back electromotive force to generate a buffer voltage, extract an attenuation coefficient from the buffer voltage, and correct the first switching position to form a second switching position according to the attenuation coefficient.

[0018] The switching execution module is used to generate a comprehensive switching control command based on the second switching position, the optimized switching time, and the energy recovery coefficient, and execute the comprehensive switching control command to complete the automatic switching of the motor power distribution system.

[0019] The beneficial effects of this invention are reflected in the following points: 1. An automatic triggering mechanism based on the auxiliary contact state is established. Combined with electric field-impedance coupling spectrum analysis technology, the electric field intensity distribution and impedance change trajectory are integrated, realizing three-dimensional monitoring of the motor insulation state and precise positioning of the field intensity concentration area. The strategy of planning the switching path at the minimum field strength point avoids the safety risks in high electric field intensity areas. Through hierarchical management of the dominant and subordinate nodes and selection of the minimum impedance gradient path, the electrical safety and path optimization effect of the switching process are ensured. 2. A conversion mechanism from arc energy density to magnetic field energy storage is established. Through real-time monitoring of the arc characteristic parameters of the vacuum circuit breaker and energy separation technology, the recovery and utilization of arc discharge energy is realized. Combined with the amplitude modulation of the back electromotive force to generate a buffer voltage, and through envelope curve analysis and inflection point sequence identification to extract the attenuation coefficient, the dynamic correction of the switching position is realized, improving the energy utilization efficiency of the system. 3. A comprehensive switching control strategy integrating spatial, temporal, and energy three-dimensional information is constructed. Through the encoding of the second switching position, the timing mark of the optimized switching time, and the compensation modulation of the energy recovery coefficient, a comprehensive switching control command is generated. This control command enables fully automatic switching of the motor power distribution system, reduces the need for manual intervention, improves the consistency and reliability of switching operations, and reduces system operating costs through energy recovery and optimized control.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0021] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0022] Unless otherwise specified or defined, the same reference numerals in different figures represent the same or similar technical features, and different reference numerals may be used to represent the same or similar technical features.

[0023] Figure 1This is a flowchart illustrating an automatic switching control method for a motor power distribution system according to the present invention.

[0024] Figure 2 This is a structural block diagram of an automatic switching control system for a motor power distribution system according to the present invention. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] The technical solutions of the embodiments of this application will be described below.

[0029] like Figure 1 As shown, this embodiment of the invention provides an automatic switching control method for a motor power distribution system, including the following steps S110-S160:

[0030] Step S110: Use an insulation monitoring device to obtain the electric field intensity distribution and leakage current path of the motor insulation medium, extract the field intensity gradient from the electric field intensity distribution, track the impedance change trajectory along the leakage current path, and superimpose the field intensity gradient and the impedance change trajectory to form an electric field-impedance coupling spectrum.

[0031] Specifically, the normally closed auxiliary contacts of the vacuum circuit breaker are connected in series to a coupler. When the state of the auxiliary contacts changes, the coupler signal triggers the insulation monitoring device to automatically determine the motor's operating status and switch operating modes. After the operating mode switch is initiated, the insulation monitoring device begins to perform in-depth electrical analysis. By arranging a multi-point electric field probe array between the motor stator windings and the casing, it monitors the spatial distribution of the electric field intensity inside the insulating medium in real time. The electric field probes use non-contact capacitive sensors, and the sensor heads are equipped with shielded electrodes to effectively suppress external electromagnetic interference. The probe array is arranged in a cylindrical coordinate system, forming a three-dimensional monitoring grid covering the entire insulation area, with uniform radial, circumferential, and axial distribution to ensure the integrity of the monitoring range. The insulation monitoring device is equipped with a high-impedance preamplifier and a high-precision analog-to-digital converter, providing a wide measurement range and high measurement accuracy. Simultaneously, the leakage current path is measured by arranging miniature current sensors on the insulation surface, using the Hall effect principle to detect the direction and magnitude of the leakage current. The current sensors are manufactured using a flexible substrate, with an ultra-thin design that fits snugly against the insulation surface, covering a wide measurement range from microamps to milliamps. The data acquisition system adopts a synchronous triggering method to ensure the temporal synchronization and spatial correlation of electric field intensity distribution and leakage current path data.

[0032] In some embodiments, the step of superimposing the field strength gradient with the impedance change trajectory to form an electric field-impedance coupling spectrum includes: constructing an electric field distribution vector field based on the field strength gradient; forming a field-path coupling relationship by combining the impedance change trajectory and the electric field distribution vector field; extracting a distortion concentration region within the field-path coupling relationship; and forming an electric field-impedance coupling spectrum based on the distribution density of the distortion concentration region.

[0033] The electric field distribution vector field is constructed based on the electric field intensity gradient. The electric field intensity vector adopts fundamental physical relationships. Calculate, where E is the electric field intensity vector and φ is the electric potential scalar field. The gradient operator is used. The electric field gradient is calculated using the potential difference between adjacent monitoring points, and the radial, circumferential, and axial gradient components are calculated separately. A complete electric field gradient tensor is constructed using these three gradient components. The discrete electric field gradient data is spatially continuous using a high-precision interpolation method to generate a smooth electric field distribution vector field. The electric field distribution vector field is represented by an arrow diagram, where the arrow direction indicates the electric field direction, the arrow length indicates the magnitude of the electric field, and the color intensity indicates the strength of the electric field gradient. Divergence analysis is performed on the vector field to identify charge accumulation regions, and curl analysis is performed to detect the non-conservative characteristics of the field distribution. A streamline diagram of the vector field is established to visually display the spatial structure and strength variation of the electric field through the distribution pattern of the electric field lines. The visualization of the vector field reveals the complex distribution pattern of the electric field inside the insulating medium, laying the foundation for subsequent field-path coupling analysis.

[0034] A field-path coupling relationship is formed by combining the impedance change trajectory and the electric field distribution vector field. The impedance change trajectory obtained by tracing the leakage current path is spatially superimposed with the constructed electric field distribution vector field for analysis. A three-dimensional mathematical description of the leakage current path is established, using parametric curve equations to represent the spatial direction of the current path. At each location point along the impedance change trajectory, the corresponding electric field distribution vector field value is extracted, establishing a ternary relationship between location coordinates, impedance value, and electric field strength. The correlation between electric field strength and impedance change is calculated, and statistical analysis methods are used to evaluate the degree of correlation. A coupling strength function between electric field strength and impedance value is constructed to quantify the local coupling degree between the electric field and impedance. The variation law of coupling strength along the path is analyzed, identifying the peak position and gradient of coupling strength. An electric field-current density relationship J=σE is established, where J is the current density vector, σ is the conductivity, and E is the electric field strength vector, while σ=1 / Z, where Z is the impedance value. Through field-path coupling relationship analysis, the spatial correspondence between the electric field concentration region and the high impedance region is identified, revealing the physical mechanism and evolution law of insulation degradation.

[0035] Distortion concentration regions are extracted within the field-path coupling relationship. Based on the established field-path coupling relationship, spatial regions where the electric field and impedance distributions significantly deviate from the normal mode are identified. A distortion degree index D = |E_measured - E_theoretical| / E_theoretical is defined, where D is the distortion degree index, E_measured is the measured electric field strength, and E_theoretical is the theoretical uniform electric field strength. When the distortion degree index exceeds a preset threshold, the region is determined to be a distortion concentration region. Cluster analysis is used to spatially group the distortion points, dividing them into different concentration regions. The geometric characteristics of each distortion concentration region are calculated, including spatial distribution characteristics such as region area, centroid location, principal axis direction, and eccentricity. The electric field strength distribution characteristics of the distortion concentration regions are analyzed, and the mean, standard deviation, peak value, and gradient rate of change of the electric field strength within the region are statistically analyzed. The influence range of the distortion concentration regions on the surrounding electric field distribution is assessed, and the influence boundary and propagation characteristics are determined through the electric field disturbance attenuation law. Establish a grading system for areas of concentrated distortion, classifying them into three levels: slight, moderate, and severe, based on the degree of distortion and the extent of impact. Record the location coordinates, distortion intensity, extent of impact, and grading results for each area of ​​concentrated distortion.

[0036] An electric field-impedance coupling map is generated based on the distribution density of distortion concentration areas. The spatial density distribution of these areas is calculated using kernel density estimation, and statistical analysis is used to determine the degree of clustering and distribution pattern of distortion points. A coordinate system is established for the map, with the horizontal axis representing radial position and the vertical axis representing axial position. Colors or contour lines indicate the intensity of distortion density. Different levels of distortion concentration areas are marked on the electric field-impedance coupling map, using different colors and symbols to distinguish between slightly, moderately, and severely distorted areas. Lines of equal distortion density are drawn, connecting spatial points with the same distortion density to form a contour map of the distortion distribution. The electric field distribution vector field and impedance change trajectory information are overlaid, simultaneously displaying the electric field direction, intensity distribution, and impedance change path on the same map. A mathematical expression for the map, comprehensively considering electric field intensity, impedance distribution, and distortion degree, is established to achieve a unified representation of multiple physical quantities. The generated electric field-impedance coupling map visually displays the coupling characteristics of the electric field and impedance within the insulating medium, clearly identifying the spatial location and severity of weak insulation areas, providing a visual analysis tool for insulation condition assessment, fault location, and preventative maintenance.

[0037] Step S120: Identify the field strength concentration region through the electric field-impedance coupling spectrum, search for the minimum field strength point in the field strength concentration region, plan the switching path through the minimum field strength point, and determine the first switching position according to the impedance distribution on the switching path.

[0038] Specifically, regions of concentrated electric field strength are identified using the electric field-impedance coupling (EV-IFF) spectrum. An adaptive threshold segmentation method is applied to the EV-IFF spectrum, setting the threshold for determining concentrated field strength to 1.5 times the mean field strength in the EV-IFF spectrum. Connected regions exceeding this threshold are identified as concentrated field strength regions based on the color distribution information of the EV-IFF spectrum. Morphological opening and closing operations are used on the EV-IFF spectrum to filter noise and smooth boundaries in the identified regions, eliminating isolated noise points and internal voids. Based on the spatial coordinate information of the EV-IFF spectrum, geometric feature parameters, including region area, perimeter, roundness, and major axis direction, are calculated for each concentrated field strength region. Using the gradient information of the EV-IFF spectrum, a region growing algorithm is used to expand the boundary of the concentrated field strength region from the seed point until the field strength value in the EV-IFF spectrum falls below the expansion threshold. The field strength distribution characteristics within the concentrated field strength regions are statistically analyzed in the EV-IFF spectrum, including the maximum, minimum, mean, and standard deviation. Based on the numerical analysis of the electric field-impedance coupling spectrum, a hierarchical classification system for the field strength concentration region is established, dividing the region in the electric field-impedance coupling spectrum into first-level, second-level, and third-level concentration regions.

[0039] The algorithm searches for the minimum field strength point within the concentrated field strength region. Within the identified concentrated field strength region, it systematically searches for local minimum field strength points as potential safe switching nodes. It ensures that a local extremum search algorithm is used within the concentrated field strength region, traversing each grid point to find the local minimum field strength. The minimum field strength point found within the concentrated field strength region is spatially verified to check whether a local depression has indeed formed in its neighborhood. The stability index of each minimum field strength point within the concentrated field strength region is calculated, and its stability is assessed by analyzing the directionality of the gradient in its neighborhood. A safety assessment system for the minimum field strength point within the concentrated field strength region is established, comprehensively considering the numerical value of the minimum field strength point within the concentrated field strength region and the electrical characteristics of the surrounding environment. Multiple minimum field strength points within the concentrated field strength region are ranked by importance, arranged from smallest to largest field strength value. Spatial clustering methods are used to group the minimum field strength points within the concentrated field strength region, merging points that are close together into the same candidate region. Record detailed information for each minimum field strength point within the field strength concentration area, including the precise coordinates, field strength value, and stability index within the field strength concentration area.

[0040] In some embodiments, the step of planning a switching path via the minimum field strength point includes: performing cluster analysis on the minimum field strength point to form a dominant node and subordinate nodes; establishing candidate paths from the dominant node to the subordinate nodes; detecting impedance changes along the candidate paths to form a set of abrupt change points; and selecting the path with the smallest impedance gradient from the set of abrupt change points as the switching path.

[0041] Cluster analysis is performed on the points with the lowest electric field strength to form dominant and subordinate nodes. All the minimum electric field strength points obtained from the search are used as input data, and hierarchical clustering is employed for node grouping and hierarchical analysis. The Euclidean distance matrix between the minimum electric field strength points is calculated as d = √[(x1-x2)² + (y1-y2)² + (z1-z2)²], where d is the distance between points, and (x1, y1, z1) and (x2, y2, z2) are the spatial coordinates of the two points, constructing a complete mapping relationship between point distances. The Ward link criterion is used for cluster merging, which determines the optimal clustering result by minimizing the intra-cluster variance. A clustering distance threshold is set to 0.8 times the average distance between the minimum electric field strength points; points with a distance less than this threshold are merged into the same node. In each cluster, the point with the lowest electric field strength is selected as the dominant node, and the remaining points are subordinate nodes. The dominant node represents the location with the most stable electric field in the region, possessing the advantage of serving as a switching start or end point. Calculate the influence radius of the dominant node; subordinate nodes within this radius are governed by the dominant node. Establish a hierarchical relationship diagram between the dominant and subordinate nodes, clarifying the dominance and subordination relationships among the nodes. Rank the dominant nodes by importance, comprehensively considering the node's field strength value, influence radius, and the number of subordinate nodes.

[0042] Candidate paths are established from the dominant node to the subordinate nodes. The A* path search algorithm is used to calculate the optimal candidate path from the dominant node to the subordinate node. A heuristic function is used to optimize the search efficiency of the candidate path by combining Euclidean distance and electric field intensity changes. Strict constraints are set during the candidate path planning process to ensure that the candidate path does not cross high-risk areas with concentrated electric field intensity and must detour or choose a safe passage. The geometric parameters of each candidate path from the dominant node to the subordinate node are calculated, including the total length of the candidate path, the number of turns, the maximum curvature, and the average curvature. The electric field environment characteristics of the candidate paths are evaluated, and the electric field intensity distribution, gradient changes, and the number of outliers on the candidate paths are statistically analyzed. A cost function for the candidate paths is established, which comprehensively considers multiple factors such as candidate path length, electric field risk, and geometric complexity. All candidate paths from the dominant node to the subordinate node are prioritized, and the candidate paths with smaller cost function values ​​are selected as the preferred path set. Detailed information for each candidate path is recorded, including the candidate path coordinate sequence, intermediate node positions, and key feature parameters.

[0043] Impedance changes are detected along candidate paths to form a set of abrupt change points. Along the established candidate paths, impedance values ​​are detected point-by-point to identify locations where sudden impedance changes occur. A sliding window technique is used, moving along the candidate path with a window length set to 5% of the total path length, to calculate the statistical characteristics of impedance values ​​within the window. The impedance abrupt change intensity on the candidate path is defined as I = |Z(i+1) - Z(i)| / σ, where I is the abrupt change intensity, Z(i) is the impedance value at the i-th sampling point, and σ is the impedance standard deviation. Abrupt change point is identified as an impedance abrupt change point on the candidate path when the abrupt change intensity I > 2. First-order and second-order difference methods are used to detect the gradient and acceleration of impedance changes along the candidate path, identifying inflection points. Cluster analysis is performed on the detected abrupt change points on the candidate path, merging closely spaced abrupt change points into abrupt change regions to avoid redundant detection. The abrupt change intensity index for each abrupt change point on the candidate path is calculated to reflect the severity and scope of the impedance change. A classification system for mutation points on candidate paths is established, classifying them into three categories based on mutation intensity: slow-change, rapid-change, and jump-change. All detected mutation points on candidate paths are aggregated to form a complete mutation point set, which records the location coordinates, mutation intensity, and classification type of each mutation point on each candidate path.

[0044] For example, selecting the path with the smallest impedance gradient from the set of mutation points as the switching path includes: identifying the impedance change characteristics of each path according to the set of mutation points; constructing a path smoothness matrix based on the impedance change characteristics; extracting a curvature sequence from the path smoothness matrix; and determining the path corresponding to the minimum value in the curvature sequence as the switching path.

[0045] The impedance change characteristics of each path are identified based on the set of mutation points. Statistical data from the mutation point set are used to calculate the distribution pattern of mutation points on each candidate path, and to statistically analyze the proportion and density of abrupt, gradual, and jump-type mutation points. Spatial information from the mutation point set is used to analyze the distribution pattern of mutation points on candidate paths, identifying the locations of dense and sparse mutation regions. Clustered data from the mutation point set is used to calculate the clustering index and dispersion degree of mutation points, quantifying the spatial clustering characteristics of the mutation point set. Intensity information from the mutation point set is used to calculate the frequency domain characteristics of impedance changes on candidate paths, and Fast Fourier Transform is used to analyze the main frequency components of the mutation point set. Time-domain statistical features are extracted from the mutation point set, including the peak intensity, valley depth, rising slope, and falling slope of impedance changes. An impedance change feature description vector based on the mutation point set is established, with each candidate path corresponding to a mutation point set containing a comprehensive feature vector including mutation density, frequency characteristics, and statistical parameters. Through similarity analysis of the mutation point set, candidate paths with similar impedance change patterns are grouped to identify the change patterns and common characteristics within the mutation point set.

[0046] A path smoothness matrix is ​​constructed based on impedance change characteristics. The impedance change characteristic parameters of each candidate path are systematically organized, and a structured path smoothness matrix is ​​constructed using statistical data of impedance change characteristics. A comprehensive evaluation function S = Σwᵢ·fᵢ is established, where S is the comprehensive smoothness score, wᵢ is the weight coefficient of the i-th indicator, and fᵢ is the standardized value of the i-th smoothness indicator. Based on the gradient information of impedance change characteristics, a multi-level evaluation index system for the path smoothness matrix is ​​established, including dimensions such as gradient uniformity, abrupt change density, and frequency component concentration. Using the frequency components of impedance change characteristics, each evaluation index of the path smoothness matrix is ​​standardized, and the Z-score standardization method is used to map the different dimensional indicators of impedance change characteristics to a unified interval. Based on the continuity parameters of impedance change characteristics, the weight coefficients of each evaluation index of the path smoothness matrix are determined using the analytic hierarchy process (AHP). Applying the main patterns of impedance change characteristics, the path smoothness matrix is ​​dimensionality-reduced, and principal component factors reflecting the essential characteristics of impedance change characteristics are extracted. By comprehensively analyzing impedance change characteristics, a comprehensive smoothness score is calculated for each candidate path in the path smoothness matrix, combining multiple impedance change characteristic indicators into a single value. A mathematical expression for the path smoothness matrix based on impedance change characteristics is established, where each element of the path smoothness matrix represents the smoothness evaluation result of a specific path under a specific impedance change characteristic indicator.

[0047] Curvature sequences are extracted from the path smoothness matrix. Based on the constructed path smoothness matrix, the comprehensive smoothness of each candidate path is extracted from the scoring data of the path smoothness matrix, forming a one-dimensional numerical sequence reflecting the overall smoothness characteristics of the path. The top few rows of data with the best smoothness in the path smoothness matrix are identified, corresponding to candidate paths with good curvature performance. The curvature distribution of the candidate paths is calculated using the gradient uniformity index in the path smoothness matrix, combined with geometric curvature. Using the frequency component data of the path smoothness matrix, the local curvature value κ=|dθ / ds| at each sampling point on the path is calculated using the three-point circular arc method, where κ is the curvature, θ is the tangent angle, and s is the arc length parameter. Combining the change continuity parameter in the path smoothness matrix, the statistical characteristics of the curvature distribution of each candidate path are statistically analyzed, including average curvature, maximum curvature, and curvature variation coefficient. Based on the comprehensive score of the path smoothness matrix, a correlation analysis between geometric curvature and impedance change smoothness is established to study the relationship between path geometry and electrical characteristic changes. This paper proposes a comprehensive evaluation method that integrates multidimensional indicators and geometric curvature in the path smoothness matrix through weighted fusion. A standardized curvature sequence is generated from the evaluation results of the path smoothness matrix, with each sequence element incorporating evaluation data from the path smoothness matrix and geometric curvature features. The curvature sequences are then sorted and filtered to identify the top candidate paths with the best curvature performance.

[0048] The path corresponding to the minimum value in the curvature sequence is determined as the switching path. Multiple verifications are performed on the minimum value of the curvature sequence, confirming the superior performance of this path through independent geometric and electrical analyses. The engineering feasibility of the path corresponding to the minimum value of the curvature sequence is examined, evaluating its safety, accessibility, and economy in practical operation. Sensitivity analysis of the switching path is conducted to study the impact of small changes in parameters in the curvature sequence on switching performance. A performance evaluation report for the switching path is established, recording the geometric parameters, electrical characteristics, safety indicators, and operational requirements of the optimal path in the curvature sequence. The candidate path with the smallest value in the curvature sequence is formally determined as the final switching path, completing the decision-making process for switching path selection. The determined switching path is refined and optimized, further improving the smoothness and safety of the optimal path in the curvature sequence through local path adjustments. A standardized description document for the switching path is established, including the coordinates, technical parameters, operating procedures, and safety precautions of the optimal path in the curvature sequence.

[0049] The first switching position is determined based on the impedance distribution along the switching path. The impedance change rate dZ / ds along the switching path is calculated, where dZ is the impedance change and ds is the path element, identifying the stable region with the smallest change rate along the switching path. Equal-interval sampling is performed along the switching path, with the sampling point spacing set to 2% of the total switching path length to ensure sufficient resolution of the impedance distribution. The impedance value and impedance gradient at each sampling point along the switching path are calculated, establishing the complete impedance distribution function Z(s) along the switching path. A smoothing filter is used to denoise the impedance distribution curve along the switching path, eliminating the influence of measurement noise and local disturbances. The morphological characteristics of the impedance distribution curve along the switching path are analyzed to identify relatively stable plateau regions and rapidly changing transition regions. Selection criteria for the switching position along the switching path are established, prioritizing positions with moderate impedance values, small gradients, and good local stability. Considering the impedance characteristics, geometric location, and operational convenience along the switching path, a candidate set of points for the first switching position is determined. A safety assessment is conducted on candidate points along the switching path, analyzing the electrical safety margin and operational risk level of each location. A multi-criteria decision-making method is used to comprehensively rank the candidate points along the switching path, selecting the location with the best overall evaluation as the first switching location.

[0050] Step S130: Monitor the arc characteristic parameters when the normally closed auxiliary contact of the vacuum circuit breaker breaks, separate the arc energy density from the arc characteristic parameters, convert the arc energy density into magnetic field energy storage, and generate an energy recovery coefficient based on the accumulation rate of magnetic field energy storage.

[0051] Specifically, the arc characteristic parameters of the normally closed auxiliary contacts of the vacuum circuit breaker are monitored during the breaking process. During the breaking process, multi-dimensional characteristic data of the arc discharge are simultaneously acquired using a high-frequency current sensor and a high-voltage probe. Taking advantage of the unique structure of the normally closed auxiliary contacts, a fiber optic spectrometer is installed at the contact gap to monitor the spectral changes and intensity distribution of the arc characteristic parameters in real time during the breaking process. A Hall effect magnetic field sensor array is arranged around the normally closed auxiliary contacts to capture the magnetic field component in the arc characteristic parameters during the breaking process. A high-speed camera system records the changes in arc characteristic parameters during the breaking process, analyzing the morphological evolution of the arc column. A broadband voltage transformer is configured to monitor the voltage waveform in the arc characteristic parameters between the normally closed auxiliary contacts of the vacuum circuit breaker, with the sampling frequency set to the megahertz level. A multidimensional data model of arc characteristic parameters during the breaking of the normally closed auxiliary contact of a vacuum circuit breaker is established. These parameters include peak current, duration, voltage gradient, magnetic field strength, spectral power density, and geometric dimensions. The arc characteristic parameters collected during the breaking of the normally closed auxiliary contact of the vacuum circuit breaker are processed synchronously to ensure the consistency of the time reference for all types of arc characteristic parameter data.

[0052] Arc energy density is separated from arc characteristic parameters. Based on the arc characteristic parameters obtained from monitoring, the distribution characteristics of arc energy density are extracted from the arc characteristic parameters using energy analysis methods. Using the current and voltage data from the arc characteristic parameters, the instantaneous arc power P(t) = U(t)·I(t) is calculated, providing a power basis for arc energy density calculation. Through spectral data analysis of the arc characteristic parameters, the contribution of different wavelengths to the arc energy density is identified, and a spectral distribution function of the arc energy density is established. Combining the geometric dimension information from the arc characteristic parameters, the volume change of the arc column is calculated, and the total energy is distributed to obtain the arc energy density ρ = E / V, where ρ is the arc energy density, E is the total energy, and V is the arc volume. Using the magnetic field strength data from the arc characteristic parameters, the influence of the magnetic field on the arc energy density distribution is analyzed, and the concentrated and diffuse regions of the arc energy density are identified. Frequency domain decomposition of the arc characteristic parameters is performed to extract the contribution of different frequency components to the arc energy density, establishing the frequency domain spectrum of the arc energy density. By analyzing the time series of arc characteristic parameters, the temporal evolution of arc energy density is tracked, and key moments of arc energy density accumulation and release are identified. A correlation model between arc energy density and arc characteristic parameters is established to achieve accurate separation and quantitative calculation of arc energy density.

[0053] The energy density of an electric arc is converted into magnetic field energy storage. Using spatial distribution data of the electric arc energy density, the current density distribution is calculated based on the current intensity and cross-sectional area within the arc region, providing a current basis for magnetic field energy storage calculations. A spatial mapping relationship of magnetic field intensity is constructed using the gradient information of the electric arc energy density, and the correlation between magnetic induction intensity and magnetic field energy storage is established using Ampere's circuital law. Taking into account the heat loss during the electric arc energy density conversion process, a conversion efficiency model is established to calculate the effective portion of the electric arc energy density that can actually be converted into magnetic field energy storage. Based on the time-varying characteristics of the electric arc energy density, the dynamic accumulation process of magnetic field energy storage is analyzed, and the magnetic field energy storage density is established as u = B² / (2μ0), where u is the magnetic field energy storage density, B is the magnetic induction intensity, and μ0 is the vacuum permeability. Through spatial gradient analysis of the electric arc energy density, the distribution pattern of magnetic field energy storage is identified, and the concentrated region and diffusion boundary of magnetic field energy storage are determined. The converted magnetic field energy storage is spatially integrated to calculate the total magnetic field energy storage W = ∫udV, where W is the total magnetic field energy storage, u is the magnetic field energy storage density, and V is the integral volume, obtaining the complete magnetic field energy storage distribution. Establish a conversion mapping table from electric arc energy density to magnetic field energy storage, and record the energy flow and loss distribution during the magnetic field energy storage conversion process.

[0054] In some embodiments, generating an energy recovery coefficient based on the accumulation rate of the magnetic field energy storage includes: performing time-domain analysis on the accumulation rate of the magnetic field energy storage to obtain instantaneous power changes; evaluating the energy conversion efficiency based on the instantaneous power changes to form an efficiency curve; extracting peak intervals from the efficiency curve to generate energy accumulation points; and generating an energy recovery coefficient based on the time distribution of the energy accumulation points.

[0055] The accumulation rate of magnetic field energy storage is analyzed in the time domain to obtain instantaneous power changes. High-precision time-domain sampling of the accumulation rate is performed with sampling intervals set at the microsecond level to ensure the capture of details of the transient changes in the accumulation rate. A moving average is applied to the accumulation rate to eliminate interference from high-frequency noise in the instantaneous power change calculation. The accumulation rate of magnetic field energy storage is correlated with the arc power at the corresponding moment to calculate the instantaneous power change ΔP = P(t + Δt) - P(t), where ΔP is the instantaneous power change and Δt is the time interval. A mathematical model of the relationship between the accumulation rate of magnetic field energy storage and the instantaneous power change is established to identify the influence of the accumulation rate on the instantaneous power change. Spectral analysis of the accumulation rate of magnetic field energy storage is performed to identify the contribution of the dominant frequency components to the instantaneous power change. Through analysis of the fluctuation characteristics of the accumulation rate of magnetic field energy storage, statistical features of the instantaneous power change are extracted, including the mean, variance, peak value, and rate of change of the instantaneous power change. Record complete time-series data of instantaneous power changes during the accumulation rate analysis of magnetic field energy storage, and establish a dynamic archive of instantaneous power changes.

[0056] Energy conversion efficiency is assessed based on instantaneous power changes to form an efficiency curve. The instantaneous energy conversion efficiency η(t) = P_out(t) / P_in(t) is calculated, where η(t) is the instantaneous efficiency, calculated in real-time using instantaneous power change data. The rate and trend of efficiency curve changes are analyzed using the gradient information of instantaneous power changes, identifying periods of efficiency increase and decrease caused by instantaneous power changes. Through statistical analysis of instantaneous power changes, a probability density function of the efficiency curve distribution is established, revealing the random impact of instantaneous power changes on the efficiency curve. Instantaneous power changes are arranged in chronological order to plot a continuous efficiency curve, with time on the horizontal axis and conversion efficiency on the vertical axis. The efficiency curve is smoothed using cubic spline interpolation to eliminate local fluctuations caused by instantaneous power changes, highlighting the main trend of the efficiency curve. The morphological characteristics of the efficiency curve are analyzed to identify the distribution location and duration of peaks, troughs, rising segments, and falling segments. A mathematical model of the efficiency curve is established, using polynomial fitting to represent the relationship between the efficiency curve and time.

[0057] Energy accumulation points are generated by extracting peak intervals from the efficiency curve. A peak detection threshold is set for the efficiency curve, and points exceeding 1.2 times the mean of the efficiency curve are selected as candidate peak points to ensure the significance of the extracted peaks. Cluster analysis is performed on the detected peak points on the efficiency curve, grouping adjacent peaks with short time intervals into peak intervals of the efficiency curve. The average efficiency and duration within each peak interval of the efficiency curve are calculated to evaluate the overall energy conversion performance within the interval. Within each peak interval of the efficiency curve, the time point with the most concentrated energy conversion is identified as the energy accumulation point, corresponding to the largest instantaneous energy storage increment. The spatial distribution characteristics of the peak intervals of the efficiency curve are analyzed, and the spatial coordinates and influence range of the energy accumulation point within the arc region are calculated. An intensity evaluation index for the energy accumulation point is established, comprehensively considering factors such as the peak height of the efficiency curve, the interval width, and the cumulative amount of the energy accumulation point. Multiple energy accumulation points extracted from the efficiency curve are ranked by importance, identifying the key energy accumulation points that contribute the most to overall energy recovery. Detailed parameters of the energy accumulation points are recorded, including their location on the efficiency curve, duration, peak efficiency, and cumulative energy.

[0058] Energy recovery coefficients are generated based on the temporal distribution of energy accumulation points. The distribution density of energy accumulation points along the time axis is analyzed, and the average interval time and distribution uniformity index of energy accumulation points are calculated. The formula for calculating the energy recovery coefficient is established: K = Σ(E_i × w_i) / E_total, where K is the energy recovery coefficient, E_i is the energy value of the i-th energy accumulation point, and w_i is the corresponding weighting factor. Based on the intensity distribution of energy accumulation points, a weighting allocation strategy is designed, with higher-intensity energy accumulation points receiving larger weight coefficients. Considering the temporal distribution characteristics of energy accumulation points, a time decay factor is introduced, appropriately increasing the weight of early-appearing energy accumulation points. Through spatial correlation analysis of energy accumulation points, a multi-point collaborative energy recovery coefficient correction mechanism is established. The generated energy recovery coefficients are normalized, limiting their numerical range to between 0 and 1 for ease of engineering application and performance comparison. A grading evaluation standard for energy recovery coefficients is established, classifying recovery performance into four levels: excellent, good, average, and poor, based on the energy recovery coefficient values. The generation process and calculation parameters of the energy recovery coefficients are recorded, forming a complete energy accumulation point distribution archive and an energy recovery coefficient evaluation report.

[0059] Step S140: Extract the real-time load impedance and power supply line impedance based on the impedance change trajectory, perform difference analysis on the real-time load impedance and power supply line impedance to generate an impedance matching window, and combine the energy recovery coefficient with the impedance matching window to determine the optimal switching time.

[0060] Specifically, real-time load impedance and power supply line impedance are extracted based on the impedance change trajectory. Time-frequency analysis of the impedance change trajectory identifies low-frequency components caused by load changes and high-frequency components caused by line characteristics. An impedance separation model is established: Z_total(t) = Z_load(t) + Z_line(t), where Z_total is the total impedance of the impedance change trajectory, Z_load is the real-time load impedance, and Z_line is the power supply line impedance. Utilizing the time-domain characteristics of the impedance change trajectory, the dynamic changes of the real-time load impedance are extracted from the trajectory using Kalman filtering. Steady-state analysis of the impedance change trajectory identifies the basic characteristic values ​​of the power supply line impedance, establishing a time-invariant model for the power supply line impedance. Wavelet decomposition is performed on the impedance change trajectory, using high-frequency details as the power supply line impedance component and low-frequency approximations as the real-time load impedance component. A parameter identification model for the impedance change trajectory is established, and the parameters of the real-time load impedance and power supply line impedance are identified in real-time using recursive least squares. The time-series data of the real-time load impedance and power supply line impedance extracted from the impedance change trajectory are recorded, creating a complete archive of the impedance separation results.

[0061] Impedance matching windows are generated by performing difference analysis between real-time load impedance and power supply line impedance. Based on the extracted real-time load impedance and power supply line impedance data, the impedance difference ΔZ(t) = |Z_load(t) - Z_line(t)| is calculated, where ΔZ is the impedance difference, reflecting the degree of matching between the real-time load impedance and the power supply line impedance. An impedance matching degree evaluation index M(t) = 1 / (1 + ΔZ(t) / Z_ref) is established, where M is the matching degree and Z_ref is the reference impedance value. The closer the matching degree is to 1, the better the real-time load impedance and the power supply line impedance are matched. A sliding window analysis is performed on the difference between the real-time load impedance and the power supply line impedance, with a window length of 10ms. The statistical characteristics of the difference between the real-time load impedance and the power supply line impedance within the window are calculated. An impedance matching threshold is set to 20% of the reference impedance. When the difference between the real-time load impedance and the power supply line impedance is less than this threshold, it is marked as a matching state. An impedance matching window is formed by time aggregation of continuous matching state points. Each impedance matching window records the start time, end time, and degree of matching between the real-time load impedance and the power supply line impedance. The distribution patterns of impedance matching windows were analyzed, and their average duration, frequency of occurrence, and stability indices were statistically analyzed. A mathematical model describing the impedance matching windows was established, and the time boundary, real-time load impedance value, power supply line impedance value, and matching quality score of each impedance matching window were recorded.

[0062] In some embodiments, determining the optimal switching time by combining the energy recovery coefficient and the impedance matching window includes: constructing an energy release time series based on the energy recovery coefficient; performing a stability assessment on the impedance matching window to form a matching reliability; modulating the energy release time series with the matching reliability as a weight to form a weighted time series; and determining the optimal switching time from the intersection of the weighted time series and the impedance matching window.

[0063] An energy release time series is constructed based on the energy recovery coefficient. The energy recovery coefficients are arranged chronologically to form a continuous-time function K(t), which describes the evolution of energy release capacity over time. First-order differencing is performed on the energy recovery coefficients to calculate the energy release rate and identify the time periods with the most dramatic changes in the coefficients. A mathematical model for the energy release time series is established: E_release(t) = K(t)·f(t), where E_release is the energy release time series and f(t) is the time modulation function. Peak detection of the energy recovery coefficients identifies key moments in energy release, which are then used as feature points of the energy release time series. Spectral analysis of the energy recovery coefficients is performed to extract dominant frequency components, constructing a periodic model of the energy release time series. A cumulative distribution function for the energy release time series is established to describe its probability distribution characteristics. Using the statistical properties of the energy recovery coefficients, a stochastic model of the energy release time series is generated to simulate its uncertainty. Complete data for the energy release time series is recorded, including timestamps, energy recovery coefficient values, release rates, and cumulative release amounts.

[0064] The stability of impedance matching windows is assessed to determine matching reliability. The standard deviation σ_ΔZ of the impedance difference within the matching window is calculated; a smaller standard deviation indicates a more stable impedance matching window. A matching reliability calculation formula is established: R(t) = exp(-σ_ΔZ / σ_ref), where R is the matching reliability, σ_ref is the reference standard deviation, and the matching reliability value is between 0 and 1. The duration distribution of the impedance matching window is analyzed; impedance matching windows with longer durations have higher matching reliability. The clarity of the impedance matching window boundaries is evaluated; impedance matching windows with blurred boundaries have lower matching reliability. The recurrence frequency of the impedance matching window is analyzed; frequently occurring matching patterns have higher matching reliability. A multi-dimensional evaluation model for the stability of impedance matching windows is established, comprehensively considering duration, matching degree, boundary clarity, and recurrence frequency. The matching reliability is normalized, mapping the matching reliability of different impedance matching windows to a unified evaluation interval. The matching reliability value of each impedance matching window is recorded, establishing a correlation database between matching reliability and impedance matching window characteristics.

[0065] A weighted time series is formed by modulating the energy release time series using matching reliability as the weight. The formula for calculating the weighted time series is established as W(t) = E_release(t)·R(t), where W is the weighted time series, E_release is the energy release time series, and R is the matching reliability. By using matching reliability as the weighting modulator, the importance of time periods with high matching reliability in the weighted time series is highlighted, while the influence of unreliable time periods is suppressed. The differences between the weighted time series and the original energy release time series are analyzed to assess the impact of matching reliability modulation on the characteristics of the weighted time series. The weighted time series is smoothed to eliminate abrupt changes caused by variations in matching reliability and to maintain the continuity of the weighted time series. A statistical model of the weighted time series is established, and the mean, variance, and probability distribution characteristics of the weighted time series are calculated. Peak analysis of the weighted time series identifies optimal time periods that simultaneously possess high energy release and high matching reliability. Time-frequency analysis is performed on the weighted time series to study the impact of weight modulation on the frequency domain characteristics of the weighted time series. The generation process and modulation parameters of the weighted time series are recorded, and a complete weight modulation archive is established.

[0066] For example, determining the optimal switching time from the intersection of the weighted time series and the impedance matching window includes: constructing a time probability field based on the weighted time series; causing the impedance matching window to cross the time probability field to generate an intersection trajectory; searching for probability peak points along the intersection trajectory to form a candidate time set; and sorting the candidate time sets according to their probability weights to generate the optimal switching time.

[0067] A time probability field is constructed based on weighted time series. The weighted time series is normalized, mapping the values ​​to a probability interval of 0 to 1, P(t) = W(t) / max(W(t)), where P is the time probability and W is the weighted time series value. A continuous distribution model of the time probability field is established, and a Gaussian kernel function is used to smooth the discrete weighted time series points through interpolation. A two-dimensional time probability field is constructed, with time on the horizontal axis and probability density on the vertical axis; the color intensity represents the strength of the time probability field distribution. Through statistical analysis of the weighted time series, statistical parameters such as the mean, variance, and skewness of the time probability field are calculated. The mathematical expression of the time probability field is established as P(t) = Σw_i·G(t-t_i,σ), where w_i is the weight, G is the Gaussian function, t_i is the sampling point of the weighted time series, and σ is the smoothing parameter. Contour analysis is performed on the time probability field to identify the distribution patterns of high-probability and low-probability regions. Record the construction parameters and distribution characteristics of the time probability field, and establish a digital representation of the time probability field.

[0068] An impedance matching window is traversed across a time probability field to generate an intersection trajectory. The time boundary information of the impedance matching window is projected onto the time probability field, and the interaction between the impedance matching window and the time probability field is analyzed. A crossing algorithm is established to simulate the movement trajectory of the impedance matching window in the time probability field, recording the probability changes during the crossing process. The intersection strength I(t) = P(t)·M(t) is calculated, where I is the intersection strength, P is the time probability field value, and M is the impedance matching window indicator function. A geometric description of the intersection trajectory is established, recording the path coordinates and direction changes of the impedance matching window as it crosses the time probability field. The morphological characteristics of the intersection trajectory are analyzed, identifying the distribution of straight segments, curved segments, and turning points. A differential equation model of the intersection trajectory evolution is established through dynamic analysis of the impedance matching window crossing the time probability field. The intersection trajectory is smoothed to eliminate abrupt changes caused by discontinuities at the impedance matching window boundary. Complete intersection trajectory data generated by the impedance matching window crossing the time probability field is recorded, including intersection trajectory coordinates, intersection strength, and geometric features.

[0069] A candidate time set is formed by searching for probability peak points along the intersecting trajectories. On the generated intersecting trajectories, a peak detection algorithm is used to search for local maxima in the probability distribution. A criterion for determining probability peak points is established: a point on the intersecting trajectory whose probability value is greater than that of its neighboring points and exceeds 1.5 times the average probability is marked as a probability peak point. First-order derivative analysis is performed on the intersecting trajectories to identify stationary points with zero probability gradients as potential probability peak points. A sliding window technique is used to move along the intersecting trajectories, searching for the point with the maximum probability within each window, forming a preliminary candidate time set for probability peak points. Cluster analysis is performed on the detected probability peak points, merging spatially close peak points into the same candidate time set. Feature parameters for each probability peak point are calculated, including peak height, half-peak width, peak sharpness, and surrounding probability gradient. A quality assessment model for probability peak points is established, comprehensively considering factors such as peak intensity, stability, and uniqueness. Points meeting quality standards are selected from all probability peak points on the intersecting trajectories to form a candidate time set. Detailed information for each time point in the candidate time set is recorded, including time location, probability value, feature parameters, and quality score.

[0070] Optimal switching times are generated by sorting the candidate time sets according to their probability weights. Based on the generated candidate time sets, a ranking criterion based on probability weights is established to select the optimal switching time. The probability weight w_i = P_i / ΣP_j for each time in the candidate time set is calculated, where w_i is the weight of the i-th candidate time in the candidate time set, and P_i is the corresponding probability value. A multi-criteria decision model is established, considering not only probability weights but also the stability, reachability, and engineering practicality of the time. The candidate time sets are comprehensively scored using the formula S_i = α·w_i + β·s_i + γ·r_i, where S is the comprehensive score, s is the stability index, r is the practicality index, and α, β, and γ are weight coefficients. The candidate time sets are sorted in descending order according to the comprehensive score, and the time with the highest score is selected as the optimal switching time. Sensitivity analysis is performed on the ranking results of the candidate time sets to study the impact of changes in weight coefficients on the ranking results, ensuring the robustness of the selection. A verification mechanism for the optimal switching time is established, and the switching performance of the selected optimal switching time is verified through simulation analysis. Record the selection process and decision-making basis for optimizing the switchover time, and establish a complete time selection archive. Determine the time with the highest ranking in the candidate time set as the final optimized switchover time, thus completing the optimization selection process for the switchover time.

[0071] Step S150: Utilize magnetic field energy storage to release reverse electromotive force at the optimized switching moment, modulate the amplitude of the reverse electromotive force to generate a buffer voltage, extract the attenuation coefficient from the buffer voltage, and correct the first switching position to form the second switching position based on the attenuation coefficient.

[0072] Specifically, magnetic field energy storage is utilized to release a back electromotive force (EMF) at the optimal switching moment. Through a rapid discharge mechanism of the stored magnetic field energy, the stored energy is released instantaneously at the optimal switching moment, generating a back EMF to suppress arc reignition. A magnetic field energy release model is established: dW / dt = -P_release, where W is the stored magnetic field energy, t is time, dt is the time derivative representing a very small change in time, dW / dt is the rate of change of the stored magnetic field energy with respect to time (i.e., the energy release rate), P_release is the released power, and the negative sign indicates a decrease in the stored magnetic field energy. The back EMF is calculated according to Faraday's law of electromagnetic induction: ε = -dΦ / dt, where ε is the back EMF, Φ is the magnetic flux, dt is the time derivative representing a very small change in time, dΦ / dt is the rate of change of the magnetic flux with respect to time (i.e., the rate of change of the magnetic flux), and the negative sign indicates the direction of the induced EMF. The magnetic field energy release controller is synchronously triggered at the optimal switching moment to ensure that the stored magnetic field energy begins to discharge at the precise optimal switching moment. By utilizing the spatial distribution information of magnetic field energy storage, the field strength distribution of the back electromotive force (EMF) in the contact gap is calculated, and the spatial effect of the back EMF is optimized. The waveform of the back EMF during the release process of the magnetic field energy storage is monitored, and the peak value, rise time, pulse width, and decay characteristics of the back EMF are recorded. Complete data on the release of the back EMF at the optimized switching moment is recorded, including the release sequence, the amplitude of the back EMF, and energy conversion parameters.

[0073] A buffer voltage is generated by amplitude modulation of the back electromotive force (EMF). Based on the obtained back EMF signal, amplitude modulation technology is used to convert the back EMF into a buffer voltage suitable for circuit protection. An amplitude modulation function U_buffer(t) = A(t)·ε(t) is established, where U_buffer is the buffer voltage, A(t) is the modulation coefficient, and ε(t) is the back EMF. The dynamic range of the modulation coefficient is determined by peak detection of the back EMF to ensure that the buffer voltage amplitude is within the safe operating range. Pulse width modulation (PWM) technology is used to shape the back EMF in the time domain, generating a buffer voltage with specific waveform characteristics. A bandpass filter is designed using the spectral characteristics of the back EMF to extract effective frequency components and suppress high-frequency noise and low-frequency drift in the back EMF. An amplitude control loop for the buffer voltage is established to dynamically adjust the modulation depth of the back EMF according to the real-time changes in load impedance. The phase relationship between the buffer voltage and the system voltage is optimized using the phase information of the back EMF to reduce voltage surges. The generated buffer voltage is waveform shaped, and digital filtering technology is used to eliminate glitches and oscillations during the back EMF modulation process. Establish quality evaluation criteria for the buffer voltage, comprehensively considering amplitude stability, waveform distortion, and spectral purity. Record the complete process of generating the buffer voltage through back electromotive force modulation, including modulation parameters, buffer voltage waveform, and quality indicators.

[0074] In some embodiments, extracting the attenuation coefficient from the buffer voltage includes: converting the buffer voltage into an envelope curve; identifying inflection points in the envelope curve to form an inflection point sequence; dividing the envelope curve into a rising segment and an attenuation segment using the inflection point sequence as a boundary; and determining the attenuation coefficient by the slope ratio of the rising segment and the attenuation segment.

[0075] The buffer voltage is converted into an envelope curve. The Hilbert transform method is used to analyze the buffer voltage, calculating its instantaneous amplitude A(t) = √[u²(t) + û²(t)], where A is the instantaneous amplitude, u is the buffer voltage signal, and û is the Hilbert transform result. A peak detection algorithm is used to identify the peak points within each period of the buffer voltage, and these peak points are connected to form the envelope of the buffer voltage. A low-pass filter is used to smooth the buffer voltage, eliminating high-frequency carrier components while preserving the main trend of the envelope curve. A mathematical model of the envelope curve is established, using piecewise functions or spline interpolation to represent its continuous changes. The envelope curve is sampled and quantized, converting the continuous envelope signal into a discrete digital sequence for subsequent processing. The statistical characteristics of the envelope curve are analyzed, calculating parameters such as mean, variance, peak factor, and crest factor. Spectral analysis of the envelope curve identifies the main frequency components and periodic characteristics of its changes. Establish the correspondence between the envelope curve and the original buffer voltage to ensure that the envelope curve accurately reflects the amplitude variation of the buffer voltage. Record the processing parameters for converting the buffer voltage into the envelope curve and the conversion quality evaluation results.

[0076] Inflection points are identified in the envelope curve to form an inflection point sequence. Based on the obtained envelope curve data, curvature analysis is used to identify the locations of inflection points on the envelope curve. The first and second derivatives of the envelope curve are calculated, and the inflection point locations are identified by the sign change of the second derivative. An inflection point determination criterion is established: when the absolute value of the second derivative of the envelope curve exceeds a threshold and the signs of the derivatives are opposite, it is marked as an inflection point. A sliding window technique is used to move along the envelope curve, detecting local extrema and inflection points within each window to form a candidate set of inflection points. The detected inflection points in the envelope curve are filtered to remove false inflection points caused by noise and retain the true inflection points of the envelope curve. A time stamp is established for the inflection point sequence, recording the time position and amplitude coordinates of each inflection point on the envelope curve. The distribution pattern of the inflection point sequence is analyzed, and the average interval, distribution density, and trend of the inflection points in the sequence are statistically analyzed. The inflection point sequence is classified according to the direction of inflection, into upward convex points, downward concave points, and mixed inflection points. Establish a mathematical description of the inflection point sequence, using vectors to represent the position, type, and characteristic parameters of each inflection point in the sequence. Record the complete inflection point sequence identified in the envelope curve, including the number, distribution, and geometric features of the inflection point sequence.

[0077] The envelope curve is divided into rising and decaying segments by using the inflection point sequence as the boundary. Peak and trough points of the envelope curve are identified within the inflection point sequence, and these key points are used as dividing lines to segment the envelope curve. Segment classification criteria are established, classifying envelope curve segments into rising and decaying segments based on the slope sign and the inflection point sequence's turning points. The slope of the rising segment is calculated as k_up = ΔA / Δt (rising), where ΔA is the amplitude change and Δt is the time interval between inflection point sequences. The slope of the decaying segment is calculated as k_down = ΔA / Δt (falling), with a negative slope indicating amplitude decay. Geometric feature analysis is performed on each rising and decaying segment, calculating segment length, average slope, rate of change of slope, and linearity. A quality assessment system for the rising and decaying segments is established to evaluate the data completeness and fitting accuracy of each segment. The start time, end time, and duration of each rising and decaying segment are recorded using the time information of the inflection point sequence. Establish a database of envelope curve segments, storing the geometric parameters, statistical characteristics, and quality indices of all rising and decaying segments. Analyze the alternation patterns of rising and decaying segments to identify the periodicity and regularity of envelope curve changes.

[0078] The attenuation coefficient is determined by the ratio of the slopes of the rising and falling segments. The formula for calculating the attenuation coefficient, β = |k_down| / |k_up|, is established, where β is the attenuation coefficient, k_down is the average slope of the falling segment, and k_up is the average slope of the rising segment. Through statistical analysis of the slopes of the rising and falling segments, the mean, standard deviation, and confidence interval of the ratio of the slopes of the rising and falling segments are calculated. A comprehensive analysis of multiple slope ratios of the rising and falling segments is performed, and a weighted average method is used to calculate the overall attenuation coefficient. A time-varying model of the attenuation coefficient is established to analyze the evolution of the slope ratio of the rising and falling segments over time. Energy analysis of the rising and falling segments verifies the physical rationality of the attenuation coefficient calculated using the slope ratio. An uncertainty assessment model for the attenuation coefficient is established to quantify the impact of measurement errors in the rising and falling segments on the calculation of the attenuation coefficient. Boundary checks are performed on the calculated attenuation coefficient to ensure that the values ​​are within the physically achievable range. Record the complete process of analyzing the slope ratio of the rising and falling segments, establish a detailed archive for calculating the attenuation coefficient, and complete the conversion calculation from the slope ratio of the rising and falling segments to the attenuation coefficient.

[0079] The first switching position is corrected based on the attenuation coefficient to form the second switching position. A position correction algorithm is established, utilizing the voltage attenuation characteristics reflected by the attenuation coefficient to calculate the impact of changes in the electrical environment at the first switching position. The position offset calculation formula is designed as Δx=f(β,Z1,E_local), where Δx is the position correction amount, β is the attenuation coefficient, Z1 is the impedance of the first switching position, and E_local is the local electric field strength. The magnitude of the attenuation coefficient is used to determine the stability of the electrical environment at the first switching position; a large attenuation coefficient indicates drastic environmental changes requiring greater correction. A search algorithm for the second switching position is established, finding the optimal second switching position within a local region near the first switching position based on the attenuation coefficient constraint. The correlation between the attenuation coefficient and the electric field distribution is used to analyze the attenuation characteristics of the electric field around the first switching position, identifying a more stable second switching position. The spatial offset direction and distance of the second switching position relative to the first switching position are determined using the gradient information of the attenuation coefficient. A safety verification mechanism for the second switching position is established to ensure that the corrected second switching position meets the safety requirements under the attenuation coefficient constraint. Record complete information about the second switching position, including coordinates, attenuation coefficient value, correction parameters, and performance evaluation. Use the second switching position corrected by the attenuation coefficient as the final switching position.

[0080] Step S160: Generate and execute a comprehensive switching control command based on the second switching position, optimized switching time, and energy recovery coefficient. Execute the comprehensive switching control command to complete the automatic switching of the motor power distribution system.

[0081] In some embodiments, generating a comprehensive switching control command based on the second switching position, the optimized switching time, and the energy recovery coefficient includes: encoding the second switching position to generate a position command code; adding a timing marker to the position command code using the optimized switching time to form a spatiotemporal command; modulating the control intensity using the energy recovery coefficient to generate an energy compensation term; and fusing the spatiotemporal command and the energy compensation term to generate a comprehensive switching control command.

[0082] The second switching position is encoded to generate a position command code. A binary encoding method is used to quantize the three-dimensional coordinates of the second switching position, establishing the encoding rule PC=encode(x,y,z), where PC is the position command code, and x, y, and z are the spatial coordinates of the second switching position. Based on the accuracy requirements of the second switching position, the bit width and resolution of the position command code are determined to ensure that the encoded position command code accurately represents the second switching position. A mapping table from the second switching position to the position command code is established, recording the unique position command code identifier corresponding to each second switching position. Boundary conditions of the second switching position are checked to ensure that the generated position command code is within the effective execution range. Direction information and execution path identifiers are added to the position command code using the geometric features of the second switching position. A verification mechanism for the position command code is established to detect errors in the second switching position encoding process through redundant encoding. The generated position command code is compressed and optimized to reduce the overhead of data transmission and storage of the second switching position. A decoding algorithm for the position command code is established to ensure that the receiving end can accurately reconstruct the second switching position information. Record the complete process of generating position instruction codes through the second switching position encoding, including encoding parameters, mapping relationships, and verification results.

[0083] A spatiotemporal instruction is formed by adding timing markers to position command codes using optimized switching times. The timing marker encoding format is established as TS=encode(T_opt,Δt,priority), where TS is the timing marker, T_opt is the optimized switching time, Δt is the time precision, and priority is the execution priority. The absolute and relative time information of the optimized switching time is embedded into the position command code, forming a spatiotemporal instruction containing a time dimension. Execution windows and time limits are set for the position command codes through timing constraints of the optimized switching time. A synchronization mechanism for the spatiotemporal instruction is established to ensure that multiple position command codes can be executed in a coordinated manner according to the requirements of the optimized switching time. Repeated execution and loop control markers are added to the spatiotemporal instruction using the periodic characteristics of the optimized switching time. A time calibration method for the spatiotemporal instruction is established, synchronizing the position command codes with a reference clock at the optimized switching time. Timing conflict detection is performed on the spatiotemporal instruction to avoid execution conflicts between different position command codes at the optimized switching time. A buffer mechanism for the spatiotemporal instruction is established to provide time fault tolerance for fluctuations in the optimized switching time. The document details the process of adding timing markers to position instruction codes to form spatiotemporal instructions using optimized switching times, including timing coding rules, synchronization parameters, and conflict handling strategies.

[0084] An energy compensation term is generated by modulating the control intensity using the energy recovery coefficient. A control intensity modulation function G(t) = G0·(1+K·α) is established, where G is the modulated control intensity, G0 is the baseline control intensity, K is the energy recovery coefficient, and α is the modulation coefficient. The power output and energy distribution strategy of the control system are dynamically adjusted based on the magnitude of the energy recovery coefficient. Utilizing the time-varying characteristics of the energy recovery coefficient, a real-time changing energy compensation term is generated to adapt to the system's energy recovery state. A calculation model for the energy compensation term is established: EC = K·P_base·β, where EC is the energy compensation term, P_base is the baseline power, and β is the compensation coefficient. The amplitude and phase of the energy compensation term are optimized through correlation analysis between the energy recovery coefficient and the system load. A limiting mechanism for the energy compensation term is established to prevent overcompensation caused by fluctuations in the energy recovery coefficient. The statistical characteristics of the energy recovery coefficient are used to predict the changing trend and adjustment direction of the energy compensation term. A feedback control loop for the energy compensation term is established to adjust the compensation parameters based on the actual recovery effect of the energy recovery coefficient. The complete algorithm for generating the energy compensation term by modulating the control intensity using the energy recovery coefficient is recorded, including the modulation function, compensation model, and constraints.

[0085] The spatiotemporal commands and energy compensation terms are fused to generate integrated handover control commands. A fusion algorithm, CMD=merge(TSI,EC,W), is established, where CMD is the integrated handover control command, TSI is the spatiotemporal command, EC is the energy compensation term, and W is the weight matrix. The position and timing information of the spatiotemporal commands provide precise execution coordinates and time references for the integrated handover control commands. Energy optimization and recovery strategies are embedded into the integrated handover control commands using the energy modulation information of the energy compensation terms. A priority coordination mechanism between the spatiotemporal commands and energy compensation terms is established to handle conflicts and competition between them during the fusion process. A weighted fusion method is used to linearly combine the spatiotemporal commands and energy compensation terms, with the weight coefficients dynamically adjusted according to the system state of the integrated handover control commands. A completeness verification mechanism for the integrated handover control commands is established to ensure that the fused commands contain complete spatiotemporal command and energy compensation term information. The format of the integrated handover control commands is standardized to unify the command structure and parameter representation. An execution simulation system for the integrated handover control commands is established to verify the effectiveness of the fusion of spatiotemporal commands and energy compensation terms. This document records the detailed process of fusing spatiotemporal commands and energy compensation terms to generate integrated switching control commands, including the fusion algorithm, weight allocation, and verification results, thus completing the final generation of integrated switching control commands.

[0086] Automatic switching of the motor power distribution system is achieved by executing integrated switching control commands. The second switching position information in the integrated switching control commands drives the switching equipment of the motor power distribution system to move to the designated position. Optimized switching time data in the integrated switching control commands precisely controls the start and completion times of the automatic switching of the motor power distribution system. Energy management and power regulation during the automatic switching process are optimized based on the energy recovery coefficient parameters in the integrated switching control commands. A real-time monitoring mechanism for the automatic switching of the motor power distribution system is established to track the execution progress of the integrated switching control commands and the response status of the automatic switching. The execution parameters of the integrated switching control commands are adjusted according to the actual status of the automatic switching through a feedback control loop. A fault detection and protection mechanism for the automatic switching of the motor power distribution system is established to ensure the safety and reliability of the automatic switching process. The execution results of the automatic switching are evaluated to verify the effectiveness of the integrated switching control commands and the performance of the automatic switching. A historical record system for the automatic switching of the motor power distribution system is established to store the integrated switching control commands, execution process, and result data for each automatic switching. Record the complete process of automatic switching of the motor power distribution system by executing integrated switching control commands, including execution parameters, monitoring data, performance indicators and system response, and complete the closed-loop control process of automatic switching of the motor power distribution system.

[0087] To implement the above-described method embodiment, an automatic switching control method for a motor power distribution system is provided to achieve the corresponding functions and technical effects. See also... Figure 2 , Figure 2 This diagram illustrates a structural block diagram of an automatic switching control system 200 for a motor power distribution system according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The automatic switching control system 200 for a motor power distribution system provided in this embodiment includes:

[0088] The electric field monitoring module 201 is used to acquire the electric field intensity distribution and leakage current path of the motor insulation medium using an insulation monitoring device, extract the field intensity gradient from the electric field intensity distribution, track the impedance change trajectory along the leakage current path, and superimpose the field intensity gradient and the impedance change trajectory to form an electric field-impedance coupling spectrum.

[0089] Path planning module 202 is used to identify the field strength concentration area through the electric field-impedance coupling spectrum, search for the minimum field strength point in the field strength concentration area, plan a switching path through the minimum field strength point, and determine the first switching position according to the impedance distribution on the switching path.

[0090] The energy recovery module 203 is used to monitor the arc characteristic parameters when the normally closed auxiliary contact of the vacuum circuit breaker is broken, separate the arc energy density from the arc characteristic parameters, convert the arc energy density into magnetic field energy storage, and generate an energy recovery coefficient based on the accumulation rate of the magnetic field energy storage.

[0091] The timing determination module 204 is used to extract the real-time load impedance and power supply line impedance based on the impedance change trajectory, perform difference analysis on the real-time load impedance and the power supply line impedance to generate an impedance matching window, and determine the optimal switching time by combining the energy recovery coefficient with the impedance matching window.

[0092] The position correction module 205 is used to release a back electromotive force at the optimized switching time by using the energy stored in the magnetic field, modulate the amplitude of the back electromotive force to generate a buffer voltage, extract an attenuation coefficient from the buffer voltage, and correct the first switching position to form a second switching position according to the attenuation coefficient.

[0093] The switching execution module 206 is used to generate a comprehensive switching control command based on the second switching position, the optimized switching time, and the energy recovery coefficient, and execute the comprehensive switching control command to complete the automatic switching of the motor power distribution system.

[0094] The aforementioned automatic switching control system 200 for a motor power distribution system can implement an automatic switching control method for a motor power distribution system according to the above-described method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining contents of this application's embodiments can be referred to the contents of the above method embodiments, and will not be repeated in this embodiment.

[0095] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0096] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. An automatic switching control method for a motor power distribution system, characterized in that, include: An insulation monitoring device is used to obtain the electric field intensity distribution and leakage current path of the motor insulation medium. The field intensity gradient is extracted from the electric field intensity distribution, and the impedance change trajectory is tracked along the leakage current path. The field intensity gradient and the impedance change trajectory are superimposed to form an electric field-impedance coupling spectrum. The electric field-impedance coupling spectrum is used to identify the field strength concentration region, the minimum field strength point is searched in the field strength concentration region, the switching path is planned through the minimum field strength point, and the first switching position is determined according to the impedance distribution on the switching path. The process involves monitoring arc characteristic parameters when the normally closed auxiliary contact of a vacuum circuit breaker breaks, separating the arc energy density from these parameters, converting the arc energy density into magnetic field energy storage, and generating an energy recovery coefficient based on the accumulation rate of the magnetic field energy storage. This includes: performing time-domain analysis on the accumulation rate of the magnetic field energy storage to obtain instantaneous power changes; evaluating the energy conversion efficiency based on the instantaneous power changes to form an efficiency curve; extracting peak intervals from the efficiency curve to generate energy accumulation points; and generating an energy recovery coefficient based on the temporal distribution of the energy accumulation points. Based on the impedance change trajectory, the real-time load impedance and power supply line impedance are extracted. The difference between the real-time load impedance and the power supply line impedance is analyzed to generate an impedance matching window. The energy recovery coefficient and the impedance matching window are combined to determine the optimal switching time. The magnetic field stores energy, releasing a back electromotive force at the optimized switching moment. The back electromotive force is then amplitude-modulated to generate a buffer voltage. An attenuation coefficient is extracted from the buffer voltage, and the first switching position is corrected based on the attenuation coefficient to form a second switching position. Extracting the attenuation coefficient from the buffer voltage includes: converting the buffer voltage into an envelope curve; identifying inflection points in the envelope curve to form an inflection point sequence; dividing the envelope curve into a rising segment and an attenuation segment using the inflection point sequence as boundaries; and determining the attenuation coefficient based on the slope ratio of the rising segment and the attenuation segment. Based on the second switching position, the optimized switching time, and the energy recovery coefficient, a comprehensive switching control command is generated, and the comprehensive switching control command is executed to complete the automatic switching of the motor power distribution system.

2. The method according to claim 1, characterized in that, The step of superimposing the electric field gradient with the impedance change trajectory to form an electric field-impedance coupling spectrum includes: Construct an electric field distribution vector field based on the stated field strength gradient; The impedance change trajectory and the electric field distribution vector field are combined to form a field-path coupling relationship; Extract the distortion concentration region within the field-path coupling relationship; An electric field-impedance coupling spectrum is formed based on the distribution density of the distortion concentration region.

3. The method according to claim 1, characterized in that, The method of planning the switching path via the minimum field strength point includes: Cluster analysis is performed on the minimum field strength points to form dominant nodes and subordinate nodes; Establish candidate paths from the dominant node to the subordinate node; A set of abrupt change points is formed by detecting impedance changes along the candidate path. The path with the smallest impedance gradient is selected from the set of mutation points as the switching path.

4. The method according to claim 1, characterized in that, The step of determining the optimal switching time by combining the energy recovery coefficient with the impedance matching window includes: Construct an energy release time series based on the energy recovery coefficient; The impedance matching window is subjected to stability evaluation to determine the matching reliability; The energy release time series is modulated using the matching reliability as a weight to form a weighted time series; The optimal switching time is determined from the intersection of the weighted time series and the impedance matching window.

5. The method according to claim 1, characterized in that, The generation of a comprehensive switching control command based on the second switching position, the optimized switching time, and the energy recovery coefficient includes: The second switching position is encoded to generate a position instruction code; The optimized switching time is used to add timing markers to the position instruction code to form a spatiotemporal instruction; An energy compensation term is generated by modulating the intensity of the energy recovery coefficient; The spatiotemporal command and the energy compensation item are fused to generate a comprehensive switching control command.

6. The method according to claim 3, characterized in that, The step of selecting the path with the smallest impedance gradient from the set of mutation points as the switching path includes: Identify the impedance change characteristics of each path based on the set of mutation points; Construct a path smoothness matrix based on the impedance change characteristics; Extract the curvature sequence from the path smoothness matrix; The path corresponding to the minimum value in the curvature sequence is determined as the switching path.

7. The method according to claim 4, characterized in that, Determining the optimal switching time from the intersection of the weighted time series and the impedance matching window includes: Construct a time probability field based on the weighted time series; The impedance matching window is made to cross the time probability field to generate an intersection trajectory; A candidate time set is formed by searching for probability peak points along the intersection trajectory; Optimized switching times are generated by sorting the candidate time sets according to their probability weights.

8. An automatic switching control system for a motor power distribution system, characterized in that, include: The electric field monitoring module is used to acquire the electric field intensity distribution and leakage current path of the motor insulation medium using an insulation monitoring device, extract the field intensity gradient from the electric field intensity distribution, track the impedance change trajectory along the leakage current path, and superimpose the field intensity gradient and the impedance change trajectory to form an electric field-impedance coupling spectrum. The path planning module is used to identify the field strength concentration area through the electric field-impedance coupling spectrum, search for the minimum field strength point in the field strength concentration area, plan a switching path through the minimum field strength point, and determine the first switching position according to the impedance distribution on the switching path. An energy recovery module is used to monitor the arc characteristic parameters when the normally closed auxiliary contact of a vacuum circuit breaker breaks, separate the arc energy density from the arc characteristic parameters, convert the arc energy density into magnetic field energy storage, and generate an energy recovery coefficient based on the accumulation rate of the magnetic field energy storage. The module includes: performing time-domain analysis on the accumulation rate of the magnetic field energy storage to obtain instantaneous power changes; evaluating the energy conversion efficiency based on the instantaneous power changes to form an efficiency curve; extracting peak intervals from the efficiency curve to generate energy accumulation points; and generating an energy recovery coefficient based on the time distribution of the energy accumulation points. The timing determination module is used to extract the real-time load impedance and power supply line impedance based on the impedance change trajectory, perform difference analysis on the real-time load impedance and the power supply line impedance to generate an impedance matching window, and combine the energy recovery coefficient with the impedance matching window to determine the optimal switching time. A position correction module is used to release a back electromotive force at the optimized switching moment using the energy stored in the magnetic field, modulate the amplitude of the back electromotive force to generate a buffer voltage, extract an attenuation coefficient from the buffer voltage, and correct the first switching position to form a second switching position based on the attenuation coefficient. Extracting the attenuation coefficient from the buffer voltage includes: converting the buffer voltage into an envelope curve; identifying inflection points in the envelope curve to form an inflection point sequence; dividing the envelope curve into a rising segment and an attenuation segment using the inflection point sequence as a boundary; and determining the attenuation coefficient by the slope ratio of the rising segment and the attenuation segment. The switching execution module is used to generate a comprehensive switching control command based on the second switching position, the optimized switching time, and the energy recovery coefficient, and execute the comprehensive switching control command to complete the automatic switching of the motor power distribution system.

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